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Visualisation Case Studies

Case Study 4: Trump vs Hillary: US Presidential Election Speeches

Are Trump’s speeches different from those of Hillary’s? The DSI performed an study on it by semantically analysing the speeches of both candidates and their interventions in debates. This analysis allowed us to create a semantic fingerprint of both candidates and their topics, and to understand what are the topics each candidate talk more about.



Case Study 5: UBIOPRED and NHLI- Bioinformatic Analysis of Severe Asthma

Check out our new video showcasing Ubiopred on the GDO. 


Case Study 3: Bitcoin


Every transaction conducted in the Bitcoin network is recorded permanently and irrevocably in a public database known as the Blockchain.  These images show the structure and associations between transactions in individual blocks of that Blockchain.  By visualizing this network of highly associated data in a large scale environment we are able to accelerate algorithmic discovery of anomalous transactional patterns, with obvious applications into areas such as fraud detection.

If you want to read further about Bitcoin through a Master's student's visit, please click here

Please read the publication Visualizing Dynamic Bitcoin Transaction Patterns written by our researchers, here.

 Check out the video explaining Bitcoin on our Youtube page! 


Bitcoin- Visualization Case Study


Case Study 2: Chinese Migration


Working with collaborators at Zhejiang University, the DSI has had access to data from a sample population of one million people. By employing various data mining and modelling techniques, researchers have been able to visualise the Chinese floating population and urbanisation over the past five years on the Data Observatory.

Results have shown that migrants to Henan have been relatively young and well educated, often from wealthy provinces like Zhejiang. After mining deeper into the data, our results indicated that more than half of these people were involved in new business start-ups, and the income of these businesses grows much faster than other cities like Beijing.

From analysing news concerning Henan over the past five years, we found that prominent terms relating to the migration to Henan Province were ‘high speed railways’, ‘business start-up policy’, ‘urbanisation’ and ‘rising strategy of central China’. It appears that the principle reasons for the young and highly-educated migrating to Henan are accessibility, business start-up oriented policies and rapid urbanisation. In turn, this has led to the province benefiting from a young and educated workforce.

The visualisation allows the presentation of this research in a format which can be easily interrogated by researchers, and communicated to non-experts. 

Case Study 1: Personalised Medicine


This demonstration presents the open-source data management and analysis system we have been developing for future personalised medicine. The system has been used widely by numerous institutions in the biomedical research and pharmaceutical industries.

The volume, complexity and heterogeneity of data generated from biomedical research require a knowledge management infrastructure which can provide effective data sharing, integration, standardisation and analysis of biomedical data.In the DSI we have been developing an open-source data management platform to support large-scale data management and complex analytical tasks for personalised medicine in clinical applications.

The data shown in this demonstration is from the Innovative Medicines Initiative (IMI) ‘Unbiased Biomarkers in the Prediction of Disease’ (U-BIOPRED) project, which contains samples and medical information from hundreds of adults and children with severe asthma. We have been working with U-BIOPRED to create a system in which the diverse data sets can be compared in an unbiased way – deploying cutting edge analytical techniques to identify different sub-types of severe asthma. The system can select a specific patient cohort based on the chosen clinical parameters, and analyse the genetic and genomic data of the patient cohort to identify the set of genes which are most likely to cause asthma. The sequences of these genes can be further analysed and the molecular interactions among these genes in the cell can be explored in the system. The findings are expected to provide novel insights into the underlying mechanisms of asthma for each patient.